Driving the future towards Driverless Cars

Have you ever wondered how many hours does a person spend driving in his entire life?

Don’t worry. I got you covered.

person driving a car.

According to a survey, In America, if a person starts driving at the age of 17 and drives until the person is nearly 79 then the person spends a whopping 37,000 hours driving the car throughout his/her life covering 1,284,256 kilometers, approximately it is the distance taken to drive to the moon and back — three times.

Imagine… if not completely, at least a part of the 37k hours being saved. Not by taking other transport mode or by appointing a driver but by opting for a self-driving car. YES!

According to another study, around the globe, nearly 1.3 million people die of road accidents each year i.,e.. on average 3,287 deaths per day.[2]

94% of such accidents were caused by human error, 2% caused by environment, 2% by the vehicles and 2% by unknown factors.[3]

And such 94% of human error can be avoided by these cars as these cars do not get distracted, they obey traffic rules, do not drive fast, do not drive drunk, they do not fall asleep (DUH!)

infographic about the 5 phases of an autonomous car.

· These self-driving cars are capable of adjusting the vehicle’s speed based on the speed of others vehicles and objects on the road, using automatic brake systems in times of emergencies making them safe for use.

· its capacity to read road-signs making it adaptable and many other features which make these cars more safe to not just the person driving but for others as well.

But how does the car manage to identify the road signs, other vehicles on the road and people too?

The LIDARs and other sensors in the car, using the computer vision, translate an image into a relevant object, features or patterns for the processing unit of the car where the driving decisions are taken. This is where Deep learning comes into the picture. Deep Learning is a machine learning technique, learning features directly from data and the methods of deep learning are proven to be more accurate than human in classifying images. So the more and more the machine works on the image the more and more features of the objects are learnt by the machine.

A machine learning model involving the training data is built by a developer for training the machine. To make this building process quicker and easy a deep learning framework is used. This framework provides components for designing, training and validating deep neural networks which use mathematical modelling to process data. Some of the popular DL frameworks are Tensorflow and keras(google), mxnet (Amazon), PyTorch (Facebook), Caffe ( Berkeley).

To make it simpler, With the help of an image recognition system, large amounts of data in the form of images are fed to the machine(car) to recognise the objects and people.

But before feeding to the machine, these images go through a process called annotation.

Tools to annotate objects even with low-quality images with precision.

It gives the developer a choice of approaching the company’s freelancer or the developer can even bring his own team.

It allows the developer to connect to cloud storage for getting things done without any interruption in model building.

These self-driving cars can be called as a revolution in the industry of automobiles and thanks to AI for such a revolutionary technology. Such revolutions are a boon to the mankind and it is our part to use such technology to the fullest making the right use of it.